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On the closest string and substring problems

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TLDR
Two polynomial-time approximationalgorithms with approximation ratio 1 + ε for any smallε to settle both the Closest String problem and the ClOSest Substring problem are presented.
Abstract
The problem of finding a center string that is "close" to every given string arises in computational molecular biology and coding theory. This problem has two versions: the Closest String problem and the Closest Substring problem. Given a set of strings S = {s1, s2, ..., sn}, each of length m, the Closest String problem is to find the smallest d and a string s of length m which is within Hamming distance d to each si e S. This problem comes from coding theory when we are looking for a code not too far away from a given set of codes. Closest Substring problem, with an additional input integer L, asks for the smallest d and a string s, of length L, which is within Hamming distance d away from a substring, of length L, of each si. This problem is much more elusive than the Closest String problem. The Closest Substring problem is formulated from applications in finding conserved regions, identifying genetic drug targets and generating genetic probes in molecular biology. Whether there are efficient approximation algorithms for both problems are major open questions in this area. We present two polynomial-time approximation algorithms with approximation ratio 1 + e for any small e to settle both questions.

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Citations
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Book

Invitation to fixed-parameter algorithms

TL;DR: This paper discusses Fixed-Parameter Algorithms, Parameterized Complexity Theory, and Selected Case Studies, and some of the techniques used in this work.
Journal ArticleDOI

Parameterized Complexity and Approximation Algorithms

TL;DR: The different ways parameterized complexity can be extended to approximation algorithms, survey results of this type and proposed directions for future research are discussed.
Journal ArticleDOI

Patternhunter II: highly sensitive and fast homology search.

TL;DR: Extending the single optimized spaced seed of PatternHunter to multiple ones, PatternHunter II simultaneously remedies the lack of sensitivity of Blastn and the Lack of speed of Smith-Waterman, for homology search.
Journal ArticleDOI

Fixed-parameter algorithms for CLOSEST STRING and related problems

TL;DR: It is shown how to solve CLOSEST STRING in linear time for fixed d —the exponential growth in d is bounded by O(dd) —the practical usefulness of the findings is substantiated by some experimental results.
References
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Book

Randomized Algorithms

TL;DR: This book introduces the basic concepts in the design and analysis of randomized algorithms and presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications.
Book

Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology

TL;DR: In this paper, the authors introduce suffix trees and their use in sequence alignment, core string edits, alignments and dynamic programming, and extend the core problems to extend the main problems.

Algorithms on strings, trees, and sequences

Dan Gusfield
TL;DR: Ukkonen’s method is the method of choice for most problems requiring the construction of a suffix tree, and it will be presented first because it is easier to understand.
Journal ArticleDOI

A workbench for multiple alignment construction and analysis.

TL;DR: An interactive program, MACAW (Multiple Alignment Construction and Analysis Workbench), that allows the user to construct multiple alignments by locating, analyzing, editing, and combining “blocks” of aligned sequence segments.
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